---
license: apache-2.0
base_model: DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000-finetuned-leukemia-1000
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 1.0
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000-finetuned-leukemia-1000

This model is a fine-tuned version of [DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000](https://huggingface.co/DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Accuracy: 1.0

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5471        | 0.9825 | 14   | 0.1240          | 0.955    |
| 0.1792        | 1.9649 | 28   | 0.0493          | 0.985    |
| 0.0936        | 2.9474 | 42   | 0.1210          | 0.965    |
| 0.0907        | 4.0    | 57   | 0.0056          | 1.0      |
| 0.0441        | 4.9825 | 71   | 0.0165          | 0.995    |
| 0.0341        | 5.9649 | 85   | 0.0059          | 0.995    |
| 0.0406        | 6.9474 | 99   | 0.0018          | 1.0      |
| 0.013         | 8.0    | 114  | 0.0200          | 0.995    |
| 0.0342        | 8.9825 | 128  | 0.0030          | 1.0      |
| 0.0246        | 9.8246 | 140  | 0.0026          | 1.0      |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cpu
- Datasets 2.19.0
- Tokenizers 0.15.2